Wetland Classification System Research Articles

Wetland Classification, Attribute Accuracy, and Scale

Quantification of all types of uncertainty helps to establish reliability in any analysis. This research focuses on uncertainty in two attribute levels of wetland classification and creates visualization tools to guide analysis of spatial uncertainty patterns over several scales. A novel variant of confusion matrix analysis compares the Cowardin and Hydrogeomorphic wetland classification systems, identifying areas and types of misclassification for binary and multivariate categories. The specific focus on uncertainty in the paper refers to categorical consistency, that is, agreement between the two classification systems, rather than comparing observed data to ground truth. Consistency is quantified using confusion matrix analysis. Aggregation across progressive focal windows transforms the confusion matrix into a multiscale data pyramid for quick determination of where attribute uncertainty is highly variant, and at what spatial resolutions classification inconsistencies emerge. The focal pyramids summarize precision, recall, and F1 scores to visualize classification differences across spatial scales. Findings show that the F1 scores appear most informative on agreement about wetlands misclassification at both coarse and fine attribute scales. The pyramid organizes multi-scale uncertainty in a single unified framework and can be “sliced” to view individual focal levels of attribute consistency. Results demonstrate how the confusion matrix can be used to quantify the percentage of a study area in which inconsistencies occur reflecting wetland presence and type. The research provides confusion metrics and display tools to focus attention on specific areas of large data sets where attribute uncertainty patterns may be complex, thus reducing land managers’ workloads by highlighting areas of uncertainty where field checking might be appropriate, and improving analytics by providing visualization tools to quickly see where such areas occur.

SMALL WETLANDS IN THE BRAZILIAN SAVANNA(CERRADO): AN UNDERESTIMATED FRESHWATER RESOURCE

In Brazil, in scientific research and public policy have largely ignored small wetlands, they are not specifically covered by current environmental laws and reference standards. Furthermore, the lack of information about, small wetlands has made their protection and management difficult. Large-scale land use changes that modify the hydrology of low-order streams often lead to the accelerated degradation and loss of small wetlands. The aim of this study conducted in Brazilian Cerrado Biome area, was to map the type, size, and distribution of small wetlands in ancatchment area). Areas represented in 12 high-resolution multispectral images were individually classified in a geographic object-based image analysis (GEOBIA), resulting in a sensitivity or recall 0.872 (~87%). The 7,132 mapped wetlands had a total area of 163km2, corresponding to a density of 4.27wetlands km−2, with smallest wetlands smaller than 4.0 hectares (90.5%). The following descriptors were applied to map small wetlands: feature: asymmetry, rectangular fit, mean nir band, compactness, density, and length. Seven types of natural wetlands and four types of man-made wetlands were accordingly identified in the study area. Their classification within a system that follows the hierarchy of the Brazilian wetland classification system is proposed. Recommendations for the future study and protection of wetlands was are also provided.

A suitable method for alpine wetland delineation: An example for the headwater area of the yellow river, Tibetan Plateau

Alpine wetlands are one of the most important ecosystems in the Three Rivers Source Area, China, which plays an important role in regulating the regional hydrological cycle and carbon cycle. Accordingly, Wetland area and its distribution are of great significance for wetland management and scientific research. In our study, a new wetland classification model which based on geomorphological types and combine object-oriented and decision tree classification model (ODTC), and used a new wetland classification system to accurately extract the wetland distributed in the Headwater Area of the Yellow River (HAYR) of the Qinghai-Tibet Plateau (QTP), China. The object-oriented method was first used to segment the image into several areas according to similarity in Pixels and Textures, and then the wetland was extracted through a decision tree constructed based on geomorphological types. The wetland extracted by the model was compared with that by other seven commonly methods, such as support vector machine (SVM) and random forest (RF), and it proved the accuracy was improved by 10%–20%. The overall classification accuracy rate was 98.9%. According to our results, the HAYR’s wetland area is 3142.3 km2, accounting for 16.1% of the study area. Marsh wetlands and flood wetlands accounted for 37.7% and 16.7% respectively. A three-dimensional map of the area showed that alpine wetlands in the research region are distributed around lakes, piedmont groundwater overflow belts, and inter-mountain catchment basin. This phenomenon demonstrates that hydrogeological circumstances influence alpine wetlands’ genesis and evolution. This work provides a new approach to investigating alpine wetlands.

Creating a Detailed Wetland Inventory with Sentinel-2 Time-Series Data and Google Earth Engine in the Prairie Pothole Region of Canada

Wetlands in the Prairie Pothole Region (PPR) of Canada and the United States represent a unique mapping challenge. They are dynamic both seasonally and year-to-year, are very small, and frequently altered by human activity. Many efforts have been made to estimate the loss of these important habitats but a high-quality inventory of pothole wetlands is needed for data-driven conservation and management of these resources. Typical landcover classifications using one or two image dates from optical or Synthetic Aperture Radar (SAR) Earth Observation (EO) systems often produce reasonable wetland inventories for less dynamic, forested landscapes, but will miss many of the temporary and seasonal wetlands in the PPR. Past studies have attempted to capture PPR wetland dynamics by using dense image stacks of optical or SAR data. We build upon previous work, using 2017–2020 Sentinel-2 imagery processed through the Google Earth Engine (GEE) cloud computing platform to capture seasonal flooding dynamics of wetlands in a prairie pothole wetland landscape in Alberta, Canada. Using 36 different image dates, wetland flood frequency (hydroperiod) was calculated by classifying water/flooding in each image date. This product along with the Global Ecosystem Dynamics Investigation (GEDI) Canopy Height Model (CHM) was then used to generate a seven-class wetland inventory with wetlands classified as areas with seasonal but not permanent water/flooding. Overall accuracies of the resulting inventory were between 95% and 96% based on comparisons with local photo-interpreted inventories at the Canadian Wetland Classification System class level, while wetlands themselves were classified with approximately 70% accuracy. The high overall accuracy is due, in part, to a dominance of uplands in the PPR. This relatively simple method of classifying water through time generates reliable wetland maps but is only applicable to ecosystems with open/non-complex wetland types and may be highly sensitive to the timing of cloud-free optical imagery that captures peak wetland flooding (usually post snow melt). Based on this work, we suggest that expensive field or photo-interpretation training data may not be needed to map wetlands in the PPR as self-labeling of flooded and non-flooded areas in a few Sentinel-2 images is sufficient to classify water through time. Our approach demonstrates a framework for the operational mapping of small, dynamic PPR wetlands that relies on open-access EO data and does not require costly, independent training data. It is an important step towards the effective conservation and management of PPR wetlands, providing an efficient method for baseline and ongoing mapping in these dynamic environments.

Using multiple taxa and wetland classification schemes for enhanced detection of biological response signatures to human impairment

Wetland indices of biological integrity (IBIs) are a common component in monitoring the wetland water resources as required by the United States’ Clean Water Act (CWA). The effectiveness of an IBI to monitor disturbance is dependent on the metrics being consistently responsive to measures of human disturbance within a described classification category. We present IBIs designed for two types of commonly used wetland classification systems – the hydrogeomorphic (HGM) and the National Wetlands Inventory (NWI). The metrics making up the IBIs were derived from anuran, avian, macroinvertebrate, and vegetation communities; each representing increasing levels of resources associated with gathering the necessary data. Knowing which communities’ data best corresponds to impairment can maximize limited wetland monitoring resources, especially if the response differs based on the wetland vegetation type NWI or HGM position on the landscape. By combining these two classification schemes together, to better define a wetland’s form and context on the landscape, more of the variability in community metrics are explained by human impairment. Moreover, when multiple taxa are used within a single wetland classification scheme, the response of the multi-taxa community IBI to the human disturbance gradient is often more sensitive than one-taxa group alone. This approach, a combination of taxa, in hybrid 2-system classification schemes, creates additional utility in measuring the effectiveness of wetland assessments and, or restoration success.

Spatio-Temporal Changes and Driving Force Analysis of Wetlands in Jiaozhou Bay

Tian, Y.; Li, J.; Wang, S.; Ai, B.; Cai, H., and Wen, Z., 2022. Spatio-temporal changes and driving force analysis of wetlands in Jiaozhou Bay. Journal of Coastal Research, 38(2), 328–344. Coconut Creek (Florida), ISSN 0749-0208. Jiaozhou Bay is the largest bay-type coastal wetland with high biodiversity in Shandong Peninsula. In recent years, the wetland of Jiaozhou Bay has been reduced dramatically due to urban expansion. It is significant to study the characteristics and causes of the wetland changes in the past 40 years for the rational exploitation and sustainable development of coastal wetland resources. The types, spatial distribution, and area information of the wetland from 1983 to 2019 are obtained by artificial visual interpretation and supervised classification using ten Landsat remote sensing images, assisted by Google images. The research analyzes the conversion of land use types and the change rule of landscape pattern in the wetland, as well as the driving forces of the wetland evolution and the uncertainty factors affecting the analysis results. The results indicate that the wetland area shows an overall decreasing trend from 1983 to 2019, 77% of which are converted into nonwetland. The wetland area increases initially, followed by a decrease from 1990 to 2000, but then continues to decrease slowly from 2000 to 2019. There are many types of land use changes from coastal tidal flats to other land use types such as construction land or pond, and the landscape pattern has been gradually fragmented. The continuous increase in temperature is the main natural cause for the degradation of the wetland. Population factor is the most important driving force for the change of the wetland. The main uncertain factors affecting the analysis results are the capture time of remote sensing image, tidal level correction, wetland classification system, and method. The results provide a basis for the decision-making of wetland protection. The quantitative data will provide a reference for the construction of a multiyear wetland dataset in Jiaozhou Bay.

Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis

A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was also observed that most of the studies were implemented over the province of Ontario. Pixel-based supervised classifiers were the most popular wetland classification algorithms. This review summarizes different RS systems and methodologies for wetland mapping in Canada to outline how RS has been utilized for the generation of wetland inventories. The results of this review paper provide the current state-of-the-art methods and datasets for wetland studies in Canada and will provide direction for future wetland mapping research.

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region.

National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images

Spatially and thematically explicit information of wetlands is important to understanding ecosystem functions and services, as well as for establishment of management policy and implementation. However, accurate wetland mapping is limited due to lacking an operational classification system and an effective classification approach at a large scale. This study was aimed to map wetlands in China by developing a hybrid object-based and hierarchical classification approach (HOHC) and a new wetland classification system for remote sensing. Application of the hybrid approach and the wetland classification system to Landsat 8 Operational Land Imager data resulted in a wetland map of China with an overall classification accuracy of 95.1%. This national scale wetland map, so named CAS_Wetlands, reveals that China’s wetland area is estimated to be 451,084 ± 2014 km2, of which 70.5% is accounted by inland wetlands. Of the 14 sub-categories, inland marsh has the largest area (152,429 ± 373 km2), while coastal swamp has the smallest coverage (259 ± 15 km2). Geospatial variations in wetland areas at multiple scales indicate that China’s wetlands mostly present in Tibet, Qinghai, Inner Mongolia, Heilongjiang, and Xinjiang Provinces. This new map provides a new baseline data to establish multi-temporal and continuous datasets for China’s wetlands and biodiversity conservation.

Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recognition; however, the use of CNNs in large-scale remote sensing landcover classifications still needs further investigation. We set out to test CNN-based landcover classification against a more conventional XGBoost shallow learning algorithm for mapping a notoriously difficult group of landcover classes, wetland class as defined by the Canadian Wetland Classification System. We developed two wetland inventory style products for a large (397,958 km2) area in the Boreal Forest region of Alberta, Canada, using Sentinel-1, Sentinel-2, and ALOS DEM data acquired in Google Earth Engine. We then tested the accuracy of these two products against three validation data sets (two photo-interpreted and one field). The CNN-generated wetland product proved to be more accurate than the shallow learning XGBoost wetland product by 5%. The overall accuracy of the CNN product was 80.2% with a mean F1-score of 0.58. We believe that CNNs are better able to capture natural complexities within wetland classes, and thus may be very useful for complex landcover classifications. Overall, this CNN framework shows great promise for generating large-scale wetland inventory data and may prove useful for other landcover mapping applications.

A Classification System for Colombian Wetlands: an Essential Step Forward in Open Environmental Policy-Making

Knowledge about the distribution and diversity of wetlands has become an essential tool for environmental management and policy-making. Yet, while recent estimates indicate that 27% of the area of Colombia is covered by wetlands and despite a number of regional studies, information about the diversity of wetlands nationally is scarce. In response, we present a national wetland classification system that is based on an ecological approach, from the perspective of wetland scientists, and which builds upon the assumptions underlying the flood pulse concept and hydrogeomorphic approach. Thus, the approach and structure of the Brazilian wetland classification system are used, with geomorphological adjustments made according to Colombia’s topography. The classification is hierarchical, multi-scale, functional, and organized according to four levels (system, macroregion, subsystem, and class), with the wetland diversity of Colombia represented nationally by 89 macrohabitats across marine-coastal, inland, and anthropogenic systems. The primary purpose of this classification is to provide integrated and organized information on the distribution and diversity of Colombian wetlands that will serve as a baseline for transparent environmental policy-making.

Canadian Wetland Inventory using Google Earth Engine: The First Map and Preliminary Results

Although wetlands provide valuable services to humans and the environment and cover a large portion of Canada, there is currently no Canada-wide wetland inventory based on the specifications defined by the Canadian Wetland Classification System (CWCS). The most practical approach for creating the Canadian Wetland Inventory (CWI) is to develop a remote sensing method feasible for large areas with the potential to be updated within certain time intervals to monitor dynamic wetland landscapes. Thus, this study aimed to create the first Canada-wide wetland inventory using Landsat-8 imagery and innovative image processing techniques available within Google Earth Engine (GEE). For this purpose, a large amount of field samples and approximately 30,000 Landsat-8 surface reflectance images were initially processed using several advanced algorithms within GEE. Then, the random forest (RF) algorithm was applied to classify the entire country. The final step was an original CWI map considering the five wetland classes defined by the CWCS (i.e., bog, fen, marsh, swamp, and shallow water) and providing updated and comprehensive information regarding the location and spatial extent of wetlands in Canada. The map had reasonable accuracy in terms of both visual and statistical analyses considering the large area of country that was classified (9.985 million km2). The overall classification accuracy and the average producer and user accuracies for wetland classes exclusively were 71%, 66%, and 63%, respectively. Additionally, based on the final classification map, it was estimated that 36% of Canada is covered by wetlands.

Assessment of Convolution Neural Networks for Wetland Mapping with Landsat in the Central Canadian Boreal Forest Region

Methods for effective wetland monitoring are needed to understand how ecosystem services may be altered from past and present anthropogenic activities and recent climate change. The large extent of wetlands in many regions suggests remote sensing as an effective means for monitoring. Remote sensing approaches have shown good performance in local extent studies, but larger regional efforts have generally produced low accuracies for detailed classes. In this research we evaluate the potential of deep-learning Convolution Neural Networks (CNNs) for wetland classification using Landsat data to bog, fen, marsh, swamp, and water classes defined by the Canada Wetland Classification System (CWCS). The study area is the northern part of the forested region of Alberta where we had access to two reference data sources. We evaluated ResNet CNNs and developed a Multi-Size/Scale ResNet Ensemble (MSRE) approach that exhibited the best performance. For assessment, a spatial extension strategy was employed that separated regions for training and testing. Results were consistent between the two reference sources. The best overall accuracy for the CWCS classes was 62–68%. Compared to a pixel-based random forest implementation this was 5–7% higher depending on the accuracy measure considered. For a parameter-optimized spatial-based implementation this was 2–4% higher. For a reduced set of classes to water, wetland, and upland, overall accuracy was in the range of 86–87%. Assessment for sampling over the entire region instead of spatial extension improved the mean class accuracies (F1-score) by 9% for the CWCS classes and for the reduced three-class level by 6%. The overall accuracies were 69% and 90% for the CWCS and reduced classes respectively with region sampling. Results in this study show that detailed classification of wetland types with Landsat remains challenging, particularly for small wetlands. In addition, further investigation of deep-learning methods are needed to identify CNN configurations and sampling methods better suited to moderate spatial resolution imagery across a range of environments.

New initiatives for Brazilian aquatic plant data management

ABSTRACT Initiatives directed at managing information on aquatic plants can support different areas of scientific research, educational practices and political decisions for sustainable management and protection of wetlands and natural resources, as well as reveal knowledge gaps regarding aquatic plants and wetlands. Furthermore, such initiatives can contribute directly to consolidating wetland classification systems in Brazil. The objectives of this work are to present and explore the use of a data repository and online platform created specifically for the management of data on aquatic plants in Brazil. The data repository for aquatic plants was developed to store information on species occurrence records in a standardized form considering: systematics, taxonomy, habit, life form, geographic distribution, hydrographic basin, wetland, bibliographic references and herbarium material. A total of 3,903 records referring to the Southeast Region of Brazil were generated for the data repository. This total encompassed 1,036 species distributed among 132 families and 466 genera. Our results illustrate the diversity of information available regarding aquatic plants, circumscribe this group of species and serve as a source of data about them. Similar databases and electronic infrastructures can be developed for other flora in the future.

The Analysis of the Fish Assemblage Characteristics by Wetland Type (River and lake) of National Wetland Classification System of Wetlands in Gyeongsangnam-do.

습지 유형에 따른 어류군집 특성을 파악하고 이를 통해 관리 전략을 마련하기 위해 경상남도에 위치한 29개의 습지 (하천형 20개소, 호수형 9개소)를 대상으로 조사를 실시하였다. 조사결과 하천형 습지에서는 평균( ${\pm}SD$ ) $10.3{\pm}4.8$ 종이, 호수형 습지에서는 평균 $9.1{\pm}4.1$ 종이 출현하였으며, 출현...

The potential of using LiDAR and color-infrared aerial imagery for palustrine wetland typology and change

Wetlands are dynamic landscapes and their spatial extent and types can change over time. Mapping wetland locations, types, and monitoring wetland typological changes have important ecological significance. The National Wetlands Inventory data suffer from two problems: the omission error that some wetlands are not mapped, and the out-of-date wetland types in many counties of the United States. To address these two problems, we proposed an automatic wetland classification model for newly mapped (or existing) wetland polygons lacking typological information. The research goals in this study were (1) to develop a nonparametric and automatic rule-based model to assign wetland types to palustrine wetlands using high-resolution remotely sensed data and (2) to quantify wetland typological changes based on the wetland types obtained from the previous step. The model is a direct application of the Cowardin et al. (1979) wetland classification system without modification. The input information for the proposed model includes Light Detection and Ranging (LiDAR)-derived vegetation height and color infrared aerial imagery-derived vegetation spectral information. We tested the model for the palustrine wetlands in Horry County, SC, and analyzed 29,090 palustrine wetland polygons (101,427 ha). The model achieved an overall agreement of 87% for wetland-type classification and showed the dynamics of wetland typological changes. This nonparametric model can be easily applied to other areas where wetland inventory needs updating.

Global Wetland Datasets: a Review

Accurate wetland datasets are indispensable for generating polices on wetland conservation and appropriate land uses, global climate change studies, and biodiversity conservation. Although increasing numbers of global wetland-related datasets have been established, the extensive disagreements among these datasets are prominent. In particular, estimates of global wetland area range from 0.54 to 21.26 million km2 and the class-specific spatial consistency of wetlands is less than 1%. The different definitions of ‘wetland’ and the intrinsic features of wetlands contribute to this extensive inconsistency. Given the various requirements of wetland-oriented data products, it is important to conduct comprehensive wetland mapping at global scale. The Ramsar wetland definition is recommended and a hierarchical and flexible structure of wetland classification system is preferred for future global wetland datasets. Time-series satellite imagery at 250–1000 m spatial resolution is preferred to characterize wetland dynamics by combination of passive/active SAR data and other ancillary data, such as topography, climate, and soil data. The classification tree method for classification of massive satellite imagery and big-data technology for sample datasets are promising for the enhancement of wetland map product accuracy.

Evaluation of a wetland classification system devised for management in a region with a high cover of peatlands: an example from the Cook Inlet Basin, Alaska

Several wetland classification schemes are now commonly used to describe wetlands in the contiguous United States to meet local, regional, and national regulatory requirements. However, these established systems have proven to be insufficient to meet the needs of land managers in Alaska. The wetlands of this northern region are predominantly peatlands, which are not adequately treated by the nationally-used systems, which have few, if any, peatland classes. A new system was therefore devised to classify wetlands in the rapidly urbanizing Cook Inlet Basin of southcentral Alaska, USA. The Cook Inlet Classification (CIC) is based on seven geomorphic and six hydrologic components that incorporate the environmental gradients responsible for the primary sources of variation in peatland ecosystems. The geomorphic and hydrologic components have the added advantage of being detectable on remote sensing imagery, which facilitates regional mapping across large tracts of inaccessible terrain. Three different quantitative measures were used to evaluate the robustness and performance of the CIC classes relative to that of other commonly used systems in the contiguous United States. The high within-group similarity of the classes identified by the CIC was clearly superior to that of the other systems, demonstrating the need for improved wetland classification systems specifically devised for regions with a high cover of peatlands.

An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery

Mapping wetlands across both natural and human-altered landscapes is important for the management of these ecosystems. Though they are considered important landscape elements providing both ecological and socioeconomic benefits, accurate wetland inventories do not exist in many areas. In this study, a multi-scale geographic object-based image analysis (GEOBIA) approach was employed to segment three high spatial resolution images acquired over landscapes of varying heterogeneity due to human-disturbance to determine the robustness of this method to changing scene variability. Multispectral layers, a digital elevation layer, normalized-difference vegetation index (NDVI) layer, and a first-order texture layer were used to segment images across three segmentation scales with a focus on accurate delineation of wetland boundaries and wetland components. Each ancillary input layer contributed to improving segmentation at different scales. Wetlands were classified using a nearest neighbor approach across a relatively undisturbed park site and an agricultural site using GeoEye1 imagery, and an urban site using WorldView2 data. Successful wetland classification was achieved across all study sites with an accuracy above 80%, though results suggest that overall a higher degree of landscape heterogeneity may negatively affect both segmentation and classification. The agricultural site suffered from the greatest amount of over and under segmentation, and lowest map accuracy (kappa: 0.78) which was partially attributed to confusion among a greater proportion of mixed vegetated classes from both wetlands and uplands. Accuracy of individual wetland classes based on the Canadian Wetland Classification system varied between each site, with kappa values ranging from 0.64 for the swamp class and 0.89 for the marsh class. This research developed a unique approach to mapping wetlands of various degrees of disturbance using GEOBIA, which can be applied to study other wetlands of similar settings.

Mapping wetland characteristics for sustainable development in Harare: The case of Borrowdale West, Highlands, National Sport stadium and Mukuvisi Woodlands wetlands

Wetland ecosystems are under threat from agriculture and urbanisation, affecting water supply and quality in urban areas like the City of Harare. With the need to protect wetlands that remain, the spatial extent of the Highlands, Borrowdale West, Mukuvisi and National Sports wetlands was established. LANDSAT and SPOT Satellite imagery were used to map the spatial extent on the wetlands and to take note of the temporal changes that occurred between 1989 and 2009. Supervised classification was used to classify the images. High resolution Google earth imagery were then downloaded and used as baseline data for mapping the wetlands using the Cowardin wetland classification system 1979 and the Land Capability Classification system for wetness. Maps showing the physical characteristic of the wetlands were then produced. Results indicated that area under wetlands has been shrinking over the years. Using the wetland classification system it was found out that much of the area under wetland should not be used for any other landuse purpose other than wetland protection. However the study found out that urban expansion projects are currently modifying the wetlands. The Highlands wetland has been drained and road construction is currently in progress while a Hotel is under construction on the National sports wetland covering 287 012 m 2 of the wetland. This study provides information on the current physical state of the wetlands, which can be the basis for wetland protection and management by policy makers and managers. Key Words: Wetland, Spatial extent, Physical characteristics, Classification, Landuse, Protection and Management